Overview

Dataset statistics

Number of variables29
Number of observations85
Missing cells95
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.4 KiB
Average record size in memory233.5 B

Variable types

Numeric9
Categorical20

Alerts

airdate has constant value "2020-12-07" Constant
_embedded_show_dvdCountry has constant value "nan" Constant
url has a high cardinality: 85 distinct values High cardinality
name has a high cardinality: 65 distinct values High cardinality
_embedded_show_url has a high cardinality: 57 distinct values High cardinality
_embedded_show_name has a high cardinality: 57 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 54 distinct values High cardinality
_links_self_href has a high cardinality: 85 distinct values High cardinality
season is highly correlated with number and 1 other fieldsHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_updated is highly correlated with seasonHigh correlation
season is highly correlated with numberHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 16 other fieldsHigh correlation
summary is highly correlated with url and 4 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
image is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
name is highly correlated with url and 5 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
id is highly correlated with url and 13 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with id and 22 other fieldsHigh correlation
season is highly correlated with url and 14 other fieldsHigh correlation
number is highly correlated with url and 18 other fieldsHigh correlation
type is highly correlated with url and 8 other fieldsHigh correlation
airtime is highly correlated with url and 17 other fieldsHigh correlation
airstamp is highly correlated with id and 23 other fieldsHigh correlation
runtime is highly correlated with url and 20 other fieldsHigh correlation
image is highly correlated with id and 24 other fieldsHigh correlation
summary is highly correlated with url and 5 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_type is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_status is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with url and 21 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with url and 22 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_weight is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 16 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
number has 1 (1.2%) missing values Missing
runtime has 4 (4.7%) missing values Missing
image has 69 (81.2%) missing values Missing
_embedded_show_runtime has 20 (23.5%) missing values Missing
_embedded_show_averageRuntime has 1 (1.2%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
image is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:03:33.111956
Analysis finished2022-05-10 02:04:03.123211
Duration30.01 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009927.271
Minimum1945591
Maximum2318099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:03.183180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1945591
5-th percentile1969402
Q11977634
median1983431
Q31996354
95-th percentile2174869.4
Maximum2318099
Range372508
Interquartile range (IQR)18720

Descriptive statistics

Standard deviation67627.26047
Coefficient of variation (CV)0.03364662068
Kurtosis6.529530899
Mean2009927.271
Median Absolute Deviation (MAD)9012
Skewness2.558998048
Sum170843818
Variance4573446359
MonotonicityNot monotonic
2022-05-09T21:04:03.293227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19792451
 
1.2%
19963491
 
1.2%
20433011
 
1.2%
20249101
 
1.2%
19963551
 
1.2%
19963541
 
1.2%
19963531
 
1.2%
19963521
 
1.2%
19963511
 
1.2%
19963501
 
1.2%
Other values (75)75
88.2%
ValueCountFrequency (%)
19455911
1.2%
19600311
1.2%
19656461
1.2%
19679281
1.2%
19690611
1.2%
19707661
1.2%
19712021
1.2%
19712031
1.2%
19712041
1.2%
19712051
1.2%
ValueCountFrequency (%)
23180991
1.2%
22111351
1.2%
22111341
1.2%
21954101
1.2%
21761241
1.2%
21698511
1.2%
21525841
1.2%
21403861
1.2%
20802231
1.2%
20790061
1.2%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
https://www.tvmaze.com/episodes/1979245/volk-1x01-seria-01
 
1
https://www.tvmaze.com/episodes/1996349/fixer-1x02-episode-2
 
1
https://www.tvmaze.com/episodes/2043301/the-college-tour-1x02-florida-tech
 
1
https://www.tvmaze.com/episodes/2024910/el-anesa-farah-2x17-episode-17
 
1
https://www.tvmaze.com/episodes/1996355/fixer-1x08-episode-8
 
1
Other values (80)
80 

Length

Max length158
Median length100
Mean length79.14117647
Min length58

Characters and Unicode

Total characters6727
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1979245/volk-1x01-seria-01
2nd rowhttps://www.tvmaze.com/episodes/1981560/volk-1x02-seria-02
3rd rowhttps://www.tvmaze.com/episodes/1986869/kotiki-1x06-seria-6
4th rowhttps://www.tvmaze.com/episodes/2140386/going-seventeen-2020-12-07-dont-lie-ii-2
5th rowhttps://www.tvmaze.com/episodes/1945591/my-little-invisible-being-1x11-episode-11

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1979245/volk-1x01-seria-011
 
1.2%
https://www.tvmaze.com/episodes/1996349/fixer-1x02-episode-21
 
1.2%
https://www.tvmaze.com/episodes/2043301/the-college-tour-1x02-florida-tech1
 
1.2%
https://www.tvmaze.com/episodes/2024910/el-anesa-farah-2x17-episode-171
 
1.2%
https://www.tvmaze.com/episodes/1996355/fixer-1x08-episode-81
 
1.2%
https://www.tvmaze.com/episodes/1996354/fixer-1x07-episode-71
 
1.2%
https://www.tvmaze.com/episodes/1996353/fixer-1x06-episode-61
 
1.2%
https://www.tvmaze.com/episodes/1996352/fixer-1x05-episode-51
 
1.2%
https://www.tvmaze.com/episodes/1996351/fixer-1x04-episode-41
 
1.2%
https://www.tvmaze.com/episodes/1996350/fixer-1x03-episode-31
 
1.2%
Other values (75)75
88.2%

Length

2022-05-09T21:04:03.418561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1979245/volk-1x01-seria-011
 
1.2%
https://www.tvmaze.com/episodes/1978779/kuad-wicha-by-brands-summer-camp-1x05-episode-51
 
1.2%
https://www.tvmaze.com/episodes/1986869/kotiki-1x06-seria-61
 
1.2%
https://www.tvmaze.com/episodes/2140386/going-seventeen-2020-12-07-dont-lie-ii-21
 
1.2%
https://www.tvmaze.com/episodes/1945591/my-little-invisible-being-1x11-episode-111
 
1.2%
https://www.tvmaze.com/episodes/2065440/the-wonderland-of-ten-thousands-4x27-episode-27-1551
 
1.2%
https://www.tvmaze.com/episodes/2080223/supreme-god-emperor-1x61-episode-611
 
1.2%
https://www.tvmaze.com/episodes/1977315/stjernestov-1x07-episode-71
 
1.2%
https://www.tvmaze.com/episodes/2003092/slepaa-10x96-kto-v-dome-zivet1
 
1.2%
https://www.tvmaze.com/episodes/1981501/esenepozner-s02-special-zakladka-39-v-poiskah-grazdanskoj-oborony-konstantinopola-i-operatora-antonioni1
 
1.2%
Other values (75)75
88.2%

Most occurring characters

ValueCountFrequency (%)
e581
 
8.6%
-509
 
7.6%
s427
 
6.3%
/425
 
6.3%
t398
 
5.9%
o364
 
5.4%
w281
 
4.2%
i277
 
4.1%
p255
 
3.8%
a254
 
3.8%
Other values (30)2956
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4522
67.2%
Decimal Number1016
 
15.1%
Other Punctuation680
 
10.1%
Dash Punctuation509
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e581
12.8%
s427
 
9.4%
t398
 
8.8%
o364
 
8.0%
w281
 
6.2%
i277
 
6.1%
p255
 
5.6%
a254
 
5.6%
m223
 
4.9%
d194
 
4.3%
Other values (16)1268
28.0%
Decimal Number
ValueCountFrequency (%)
1234
23.0%
0151
14.9%
2131
12.9%
9120
11.8%
775
 
7.4%
467
 
6.6%
664
 
6.3%
562
 
6.1%
359
 
5.8%
853
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/425
62.5%
.170
 
25.0%
:85
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4522
67.2%
Common2205
32.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e581
12.8%
s427
 
9.4%
t398
 
8.8%
o364
 
8.0%
w281
 
6.2%
i277
 
6.1%
p255
 
5.6%
a254
 
5.6%
m223
 
4.9%
d194
 
4.3%
Other values (16)1268
28.0%
Common
ValueCountFrequency (%)
-509
23.1%
/425
19.3%
1234
10.6%
.170
 
7.7%
0151
 
6.8%
2131
 
5.9%
9120
 
5.4%
:85
 
3.9%
775
 
3.4%
467
 
3.0%
Other values (4)238
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e581
 
8.6%
-509
 
7.6%
s427
 
6.3%
/425
 
6.3%
t398
 
5.9%
o364
 
5.4%
w281
 
4.2%
i277
 
4.1%
p255
 
3.8%
a254
 
3.8%
Other values (30)2956
43.9%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct65
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
Episode 1
 
4
Episode 2
 
4
Episode 7
 
3
Episode 20
 
3
Episode 3
 
3
Other values (60)
68 

Length

Max length96
Median length82
Mean length18.52941176
Min length7

Characters and Unicode

Total characters1575
Distinct characters103
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)63.5%

Sample

1st rowСерия 01
2nd rowСерия 02
3rd rowСерия 6
4th rowDon't Lie Ⅱ #2
5th rowEpisode 11

Common Values

ValueCountFrequency (%)
Episode 14
 
4.7%
Episode 24
 
4.7%
Episode 73
 
3.5%
Episode 203
 
3.5%
Episode 33
 
3.5%
Episode 53
 
3.5%
Episode 43
 
3.5%
Episode 242
 
2.4%
Episode 232
 
2.4%
Episode 192
 
2.4%
Other values (55)56
65.9%

Length

2022-05-09T21:04:03.533185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode42
 
14.8%
17
 
2.5%
26
 
2.1%
solar6
 
2.1%
the6
 
2.1%
76
 
2.1%
6
 
2.1%
car4
 
1.4%
серия4
 
1.4%
december3
 
1.1%
Other values (166)194
68.3%

Most occurring characters

ValueCountFrequency (%)
199
 
12.6%
e125
 
7.9%
i97
 
6.2%
o93
 
5.9%
s74
 
4.7%
a68
 
4.3%
d64
 
4.1%
r55
 
3.5%
n55
 
3.5%
p48
 
3.0%
Other values (93)697
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1052
66.8%
Space Separator199
 
12.6%
Uppercase Letter183
 
11.6%
Decimal Number105
 
6.7%
Other Punctuation28
 
1.8%
Dash Punctuation5
 
0.3%
Close Punctuation1
 
0.1%
Open Punctuation1
 
0.1%
Letter Number1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e125
 
11.9%
i97
 
9.2%
o93
 
8.8%
s74
 
7.0%
a68
 
6.5%
d64
 
6.1%
r55
 
5.2%
n55
 
5.2%
p48
 
4.6%
l41
 
3.9%
Other values (41)332
31.6%
Uppercase Letter
ValueCountFrequency (%)
E45
24.6%
S18
 
9.8%
C15
 
8.2%
A10
 
5.5%
T9
 
4.9%
B7
 
3.8%
G7
 
3.8%
W7
 
3.8%
F7
 
3.8%
H6
 
3.3%
Other values (21)52
28.4%
Decimal Number
ValueCountFrequency (%)
224
22.9%
121
20.0%
012
11.4%
711
10.5%
39
 
8.6%
67
 
6.7%
47
 
6.7%
97
 
6.7%
55
 
4.8%
82
 
1.9%
Other Punctuation
ValueCountFrequency (%)
,11
39.3%
'8
28.6%
#5
17.9%
:2
 
7.1%
?1
 
3.6%
&1
 
3.6%
Space Separator
ValueCountFrequency (%)
199
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1070
67.9%
Common339
 
21.5%
Cyrillic166
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e125
 
11.7%
i97
 
9.1%
o93
 
8.7%
s74
 
6.9%
a68
 
6.4%
d64
 
6.0%
r55
 
5.1%
n55
 
5.1%
p48
 
4.5%
E45
 
4.2%
Other values (39)346
32.3%
Cyrillic
ValueCountFrequency (%)
о20
 
12.0%
и18
 
10.8%
а14
 
8.4%
р10
 
6.0%
н10
 
6.0%
я8
 
4.8%
е8
 
4.8%
к7
 
4.2%
л7
 
4.2%
т6
 
3.6%
Other values (24)58
34.9%
Common
ValueCountFrequency (%)
199
58.7%
224
 
7.1%
121
 
6.2%
012
 
3.5%
711
 
3.2%
,11
 
3.2%
39
 
2.7%
'8
 
2.4%
67
 
2.1%
47
 
2.1%
Other values (10)30
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1407
89.3%
Cyrillic166
 
10.5%
None1
 
0.1%
Number Forms1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199
 
14.1%
e125
 
8.9%
i97
 
6.9%
o93
 
6.6%
s74
 
5.3%
a68
 
4.8%
d64
 
4.5%
r55
 
3.9%
n55
 
3.9%
p48
 
3.4%
Other values (57)529
37.6%
Cyrillic
ValueCountFrequency (%)
о20
 
12.0%
и18
 
10.8%
а14
 
8.4%
р10
 
6.0%
н10
 
6.0%
я8
 
4.8%
е8
 
4.8%
к7
 
4.2%
л7
 
4.2%
т6
 
3.6%
Other values (24)58
34.9%
None
ValueCountFrequency (%)
å1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.3882353
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:03.627144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)3

Descriptive statistics

Standard deviation557.7137022
Coefficient of variation (CV)3.292517342
Kurtosis7.748466286
Mean169.3882353
Median Absolute Deviation (MAD)0
Skewness3.092756979
Sum14398
Variance311044.5737
MonotonicityNot monotonic
2022-05-09T21:04:03.705334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
156
65.9%
20207
 
8.2%
47
 
8.2%
25
 
5.9%
182
 
2.4%
101
 
1.2%
301
 
1.2%
91
 
1.2%
31
 
1.2%
121
 
1.2%
Other values (3)3
 
3.5%
ValueCountFrequency (%)
156
65.9%
25
 
5.9%
31
 
1.2%
47
 
8.2%
61
 
1.2%
91
 
1.2%
101
 
1.2%
121
 
1.2%
182
 
2.4%
271
 
1.2%
ValueCountFrequency (%)
20207
8.2%
311
 
1.2%
301
 
1.2%
271
 
1.2%
182
 
2.4%
121
 
1.2%
101
 
1.2%
91
 
1.2%
61
 
1.2%
47
8.2%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36
Distinct (%)42.9%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean30.52380952
Minimum1
Maximum334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:03.815099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7.5
Q324
95-th percentile138.5
Maximum334
Range333
Interquartile range (IQR)21

Descriptive statistics

Standard deviation63.04358335
Coefficient of variation (CV)2.065390406
Kurtosis13.01573895
Mean30.52380952
Median Absolute Deviation (MAD)6.5
Skewness3.590645992
Sum2564
Variance3974.493402
MonotonicityNot monotonic
2022-05-09T21:04:03.894130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
19
 
10.6%
28
 
9.4%
57
 
8.2%
36
 
7.1%
45
 
5.9%
494
 
4.7%
64
 
4.7%
93
 
3.5%
73
 
3.5%
203
 
3.5%
Other values (26)32
37.6%
ValueCountFrequency (%)
19
10.6%
28
9.4%
36
7.1%
45
5.9%
57
8.2%
64
4.7%
73
 
3.5%
81
 
1.2%
93
 
3.5%
101
 
1.2%
ValueCountFrequency (%)
3341
1.2%
2921
1.2%
2911
1.2%
2331
1.2%
1461
1.2%
961
1.2%
791
1.2%
611
1.2%
601
1.2%
571
1.2%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size808.0 B
regular
84 
insignificant_special
 
1

Length

Max length21
Median length7
Mean length7.164705882
Min length7

Characters and Unicode

Total characters609
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular84
98.8%
insignificant_special1
 
1.2%

Length

2022-05-09T21:04:04.003840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:04:04.097592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular84
98.8%
insignificant_special1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r168
27.6%
a86
14.1%
e85
14.0%
g85
14.0%
l85
14.0%
u84
13.8%
i5
 
0.8%
n3
 
0.5%
s2
 
0.3%
c2
 
0.3%
Other values (4)4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter608
99.8%
Connector Punctuation1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r168
27.6%
a86
14.1%
e85
14.0%
g85
14.0%
l85
14.0%
u84
13.8%
i5
 
0.8%
n3
 
0.5%
s2
 
0.3%
c2
 
0.3%
Other values (3)3
 
0.5%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin608
99.8%
Common1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r168
27.6%
a86
14.1%
e85
14.0%
g85
14.0%
l85
14.0%
u84
13.8%
i5
 
0.8%
n3
 
0.5%
s2
 
0.3%
c2
 
0.3%
Other values (3)3
 
0.5%
Common
ValueCountFrequency (%)
_1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r168
27.6%
a86
14.1%
e85
14.0%
g85
14.0%
l85
14.0%
u84
13.8%
i5
 
0.8%
n3
 
0.5%
s2
 
0.3%
c2
 
0.3%
Other values (4)4
 
0.7%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size808.0 B
2020-12-07
85 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-07
2nd row2020-12-07
3rd row2020-12-07
4th row2020-12-07
5th row2020-12-07

Common Values

ValueCountFrequency (%)
2020-12-0785
100.0%

Length

2022-05-09T21:04:04.175718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:04:04.270149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-0785
100.0%

Most occurring characters

ValueCountFrequency (%)
2255
30.0%
0255
30.0%
-170
20.0%
185
 
10.0%
785
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number680
80.0%
Dash Punctuation170
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2255
37.5%
0255
37.5%
185
 
12.5%
785
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2255
30.0%
0255
30.0%
-170
20.0%
185
 
10.0%
785
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2255
30.0%
0255
30.0%
-170
20.0%
185
 
10.0%
785
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
61 
20:00
15 
21:00
 
3
12:00
 
1
06:00
 
1
Other values (4)
 
4

Length

Max length5
Median length3
Mean length3.564705882
Min length3

Characters and Unicode

Total characters303
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th row12:00

Common Values

ValueCountFrequency (%)
nan61
71.8%
20:0015
 
17.6%
21:003
 
3.5%
12:001
 
1.2%
06:001
 
1.2%
17:351
 
1.2%
00:001
 
1.2%
19:001
 
1.2%
20:151
 
1.2%

Length

2022-05-09T21:04:04.348615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:04:04.458205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan61
71.8%
20:0015
 
17.6%
21:003
 
3.5%
12:001
 
1.2%
06:001
 
1.2%
17:351
 
1.2%
00:001
 
1.2%
19:001
 
1.2%
20:151
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n122
40.3%
063
20.8%
a61
20.1%
:24
 
7.9%
220
 
6.6%
17
 
2.3%
52
 
0.7%
61
 
0.3%
71
 
0.3%
31
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter183
60.4%
Decimal Number96
31.7%
Other Punctuation24
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
063
65.6%
220
 
20.8%
17
 
7.3%
52
 
2.1%
61
 
1.0%
71
 
1.0%
31
 
1.0%
91
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
n122
66.7%
a61
33.3%
Other Punctuation
ValueCountFrequency (%)
:24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin183
60.4%
Common120
39.6%

Most frequent character per script

Common
ValueCountFrequency (%)
063
52.5%
:24
 
20.0%
220
 
16.7%
17
 
5.8%
52
 
1.7%
61
 
0.8%
71
 
0.8%
31
 
0.8%
91
 
0.8%
Latin
ValueCountFrequency (%)
n122
66.7%
a61
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n122
40.3%
063
20.8%
a61
20.1%
:24
 
7.9%
220
 
6.6%
17
 
2.3%
52
 
0.7%
61
 
0.3%
71
 
0.3%
31
 
0.3%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
2020-12-07T12:00:00+00:00
57 
2020-12-07T17:00:00+00:00
2020-12-07T00:00:00+00:00
 
3
2020-12-07T04:00:00+00:00
 
3
2020-12-07T21:00:00+00:00
 
2
Other values (12)
14 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2125
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)11.8%

Sample

1st row2020-12-07T00:00:00+00:00
2nd row2020-12-07T00:00:00+00:00
3rd row2020-12-07T00:00:00+00:00
4th row2020-12-07T03:00:00+00:00
5th row2020-12-07T04:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-07T12:00:00+00:0057
67.1%
2020-12-07T17:00:00+00:006
 
7.1%
2020-12-07T00:00:00+00:003
 
3.5%
2020-12-07T04:00:00+00:003
 
3.5%
2020-12-07T21:00:00+00:002
 
2.4%
2020-12-07T11:00:00+00:002
 
2.4%
2020-12-07T19:00:00+00:002
 
2.4%
2020-12-07T16:00:00+00:001
 
1.2%
2020-12-08T01:00:00+00:001
 
1.2%
2020-12-07T19:15:00+00:001
 
1.2%
Other values (7)7
 
8.2%

Length

2022-05-09T21:04:04.567691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-07t12:00:00+00:0057
67.1%
2020-12-07t17:00:00+00:006
 
7.1%
2020-12-07t00:00:00+00:003
 
3.5%
2020-12-07t04:00:00+00:003
 
3.5%
2020-12-07t21:00:00+00:002
 
2.4%
2020-12-07t11:00:00+00:002
 
2.4%
2020-12-07t19:00:00+00:002
 
2.4%
2020-12-07t03:00:00+00:001
 
1.2%
2020-12-07t05:00:00+00:001
 
1.2%
2020-12-07t05:35:00+00:001
 
1.2%
Other values (7)7
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0946
44.5%
2315
 
14.8%
:255
 
12.0%
-170
 
8.0%
1162
 
7.6%
789
 
4.2%
T85
 
4.0%
+85
 
4.0%
55
 
0.2%
44
 
0.2%
Other values (4)9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1530
72.0%
Other Punctuation255
 
12.0%
Dash Punctuation170
 
8.0%
Uppercase Letter85
 
4.0%
Math Symbol85
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0946
61.8%
2315
 
20.6%
1162
 
10.6%
789
 
5.8%
55
 
0.3%
44
 
0.3%
93
 
0.2%
83
 
0.2%
32
 
0.1%
61
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:255
100.0%
Dash Punctuation
ValueCountFrequency (%)
-170
100.0%
Uppercase Letter
ValueCountFrequency (%)
T85
100.0%
Math Symbol
ValueCountFrequency (%)
+85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2040
96.0%
Latin85
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0946
46.4%
2315
 
15.4%
:255
 
12.5%
-170
 
8.3%
1162
 
7.9%
789
 
4.4%
+85
 
4.2%
55
 
0.2%
44
 
0.2%
93
 
0.1%
Other values (3)6
 
0.3%
Latin
ValueCountFrequency (%)
T85
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0946
44.5%
2315
 
14.8%
:255
 
12.0%
-170
 
8.0%
1162
 
7.6%
789
 
4.2%
T85
 
4.0%
+85
 
4.0%
55
 
0.2%
44
 
0.2%
Other values (4)9
 
0.4%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct35
Distinct (%)43.2%
Missing4
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean39.60493827
Minimum2
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:04.667256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q118
median32
Q348
95-th percentile120
Maximum180
Range178
Interquartile range (IQR)30

Descriptive statistics

Standard deviation31.58111739
Coefficient of variation (CV)0.797403525
Kurtosis5.17289454
Mean39.60493827
Median Absolute Deviation (MAD)16
Skewness1.965959895
Sum3208
Variance997.3669753
MonotonicityNot monotonic
2022-05-09T21:04:04.760962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
609
 
10.6%
459
 
10.6%
304
 
4.7%
184
 
4.7%
484
 
4.7%
53
 
3.5%
233
 
3.5%
203
 
3.5%
253
 
3.5%
123
 
3.5%
Other values (25)36
42.4%
(Missing)4
 
4.7%
ValueCountFrequency (%)
21
 
1.2%
53
3.5%
81
 
1.2%
102
2.4%
111
 
1.2%
123
3.5%
131
 
1.2%
141
 
1.2%
152
2.4%
163
3.5%
ValueCountFrequency (%)
1801
 
1.2%
1301
 
1.2%
1203
 
3.5%
902
 
2.4%
631
 
1.2%
609
10.6%
521
 
1.2%
502
 
2.4%
484
4.7%
471
 
1.2%

image
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct16
Distinct (%)100.0%
Missing69
Missing (%)81.2%
Memory size808.0 B
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718016.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718016.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/293/734732.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/293/734732.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/343/857757.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/343/857757.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718042.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718042.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718019.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718019.jpg'}
 
1
Other values (11)
11 

Length

Max length178
Median length176
Mean length176.125
Min length176

Characters and Unicode

Total characters2818
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/290/726341.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/290/726341.jpg'}
2nd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718158.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718158.jpg'}
3rd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718064.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718064.jpg'}
4th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718065.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718065.jpg'}
5th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718654.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718654.jpg'}

Common Values

ValueCountFrequency (%)
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718016.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718016.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/293/734732.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/293/734732.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/343/857757.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/343/857757.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718042.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718042.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718019.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718019.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718018.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718018.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718017.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718017.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/718194.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/718194.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/290/726341.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/290/726341.jpg'}1
 
1.2%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/407/1018202.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/407/1018202.jpg'}1
 
1.2%
Other values (6)6
 
7.1%
(Missing)69
81.2%

Length

2022-05-09T21:04:04.976730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medium16
25.0%
original16
25.0%
https://static.tvmaze.com/uploads/images/medium_landscape/287/718065.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/medium_landscape/407/1018202.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/original_untouched/407/1018202.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/medium_landscape/287/718103.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/original_untouched/287/718103.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/medium_landscape/287/718654.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/original_untouched/287/718654.jpg1
 
1.6%
https://static.tvmaze.com/uploads/images/original_untouched/287/718065.jpg1
 
1.6%
Other values (24)24
37.5%

Most occurring characters

ValueCountFrequency (%)
/224
 
7.9%
a192
 
6.8%
t176
 
6.2%
m160
 
5.7%
i160
 
5.7%
s144
 
5.1%
e128
 
4.5%
'128
 
4.5%
o112
 
4.0%
p112
 
4.0%
Other values (28)1282
45.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1888
67.0%
Other Punctuation528
 
18.7%
Decimal Number290
 
10.3%
Space Separator48
 
1.7%
Connector Punctuation32
 
1.1%
Close Punctuation16
 
0.6%
Open Punctuation16
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a192
 
10.2%
t176
 
9.3%
m160
 
8.5%
i160
 
8.5%
s144
 
7.6%
e128
 
6.8%
o112
 
5.9%
p112
 
5.9%
g96
 
5.1%
c96
 
5.1%
Other values (9)512
27.1%
Decimal Number
ValueCountFrequency (%)
762
21.4%
854
18.6%
144
15.2%
240
13.8%
024
 
8.3%
318
 
6.2%
416
 
5.5%
614
 
4.8%
510
 
3.4%
98
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/224
42.4%
'128
24.2%
.96
18.2%
:64
 
12.1%
,16
 
3.0%
Space Separator
ValueCountFrequency (%)
48
100.0%
Connector Punctuation
ValueCountFrequency (%)
_32
100.0%
Close Punctuation
ValueCountFrequency (%)
}16
100.0%
Open Punctuation
ValueCountFrequency (%)
{16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1888
67.0%
Common930
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/224
24.1%
'128
13.8%
.96
10.3%
:64
 
6.9%
762
 
6.7%
854
 
5.8%
48
 
5.2%
144
 
4.7%
240
 
4.3%
_32
 
3.4%
Other values (9)138
14.8%
Latin
ValueCountFrequency (%)
a192
 
10.2%
t176
 
9.3%
m160
 
8.5%
i160
 
8.5%
s144
 
7.6%
e128
 
6.8%
o112
 
5.9%
p112
 
5.9%
g96
 
5.1%
c96
 
5.1%
Other values (9)512
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/224
 
7.9%
a192
 
6.8%
t176
 
6.2%
m160
 
5.7%
i160
 
5.7%
s144
 
5.1%
e128
 
4.5%
'128
 
4.5%
o112
 
4.0%
p112
 
4.0%
Other values (28)1282
45.5%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
62 
<p>НТВ 27.12.2021 https://www.ntv.ru/serial/Volk/</p>
 
1
<p>Managing director Barry visits the Walsall branch to help with the launch of Project Diamond - a multimillion-pound investment into giving stores a makeover and branching into new product lines like frozen and chilled food.</p>
 
1
<p>From humble beginnings to legendary baseball player and current CEO, A-Rod shares his success story.</p>
 
1
<p>Join Gus Sorola, Gavin Free, Blaine Gibson, and Barbara Dunkelman as they discuss Gus's trash internet, trying not to spoil Mandalorian, Dominick the stupid Christmas Donkey, and more on this week's RT Podcast!</p>
 
1
Other values (19)
19 

Length

Max length409
Median length3
Mean length51.61176471
Min length3

Characters and Unicode

Total characters4387
Distinct characters71
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)27.1%

Sample

1st row<p>НТВ 27.12.2021 https://www.ntv.ru/serial/Volk/</p>
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan62
72.9%
<p>НТВ 27.12.2021 https://www.ntv.ru/serial/Volk/</p>1
 
1.2%
<p>Managing director Barry visits the Walsall branch to help with the launch of Project Diamond - a multimillion-pound investment into giving stores a makeover and branching into new product lines like frozen and chilled food.</p>1
 
1.2%
<p>From humble beginnings to legendary baseball player and current CEO, A-Rod shares his success story.</p>1
 
1.2%
<p>Join Gus Sorola, Gavin Free, Blaine Gibson, and Barbara Dunkelman as they discuss Gus's trash internet, trying not to spoil Mandalorian, Dominick the stupid Christmas Donkey, and more on this week's RT Podcast!</p>1
 
1.2%
<p>Experience extreme academics and unbeatable fun-in-the-sun at Florida Institute of Technology in Melbourne, FL - a short drive from the beach, Kennedy Space Center, and big opportunities. Learn about the university's hands-on degree programs, life on campus, and what it means to be a Florida Tech Panther. We'll take you underwater, into the sky, behind the wheel of a jet dragster and to Mars!<br /> </p>1
 
1.2%
<p>In the finale of our 2,000-mile race across the outback, things are about to take a sharp turn for one of the leading teams. The remaining teams are struggling to survive - but only one can win!</p>1
 
1.2%
<p>On day four, teams scramble to perform repairs as lightning, thunder, and brutal winds batter their encampment.</p>1
 
1.2%
<p>800 kilometers from the finish line, the remaining teams persist through the heat and wind-- some more successfully than others.</p>1
 
1.2%
<p>It's day two and the teams are starting to get tactical, pushing their tech to the limits and then some.</p>1
 
1.2%
Other values (14)14
 
16.5%

Length

2022-05-09T21:04:05.086710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan62
 
8.5%
the37
 
5.1%
and26
 
3.6%
to23
 
3.2%
of20
 
2.8%
a20
 
2.8%
8
 
1.1%
p7
 
1.0%
as6
 
0.8%
in6
 
0.8%
Other values (418)511
70.4%

Most occurring characters

ValueCountFrequency (%)
635
14.5%
n367
 
8.4%
e366
 
8.3%
a350
 
8.0%
t274
 
6.2%
i223
 
5.1%
s223
 
5.1%
r221
 
5.0%
o213
 
4.9%
h135
 
3.1%
Other values (61)1380
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3303
75.3%
Space Separator643
 
14.7%
Uppercase Letter154
 
3.5%
Other Punctuation131
 
3.0%
Math Symbol112
 
2.6%
Decimal Number23
 
0.5%
Dash Punctuation19
 
0.4%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T13
 
8.4%
M12
 
7.8%
S12
 
7.8%
D11
 
7.1%
B10
 
6.5%
F9
 
5.8%
A9
 
5.8%
C8
 
5.2%
P8
 
5.2%
R6
 
3.9%
Other values (17)56
36.4%
Lowercase Letter
ValueCountFrequency (%)
n367
11.1%
e366
11.1%
a350
10.6%
t274
 
8.3%
i223
 
6.8%
s223
 
6.8%
r221
 
6.7%
o213
 
6.4%
h135
 
4.1%
l127
 
3.8%
Other values (16)804
24.3%
Other Punctuation
ValueCountFrequency (%)
.39
29.8%
,36
27.5%
/35
26.7%
'12
 
9.2%
!7
 
5.3%
:2
 
1.5%
Decimal Number
ValueCountFrequency (%)
010
43.5%
27
30.4%
14
 
17.4%
71
 
4.3%
81
 
4.3%
Space Separator
ValueCountFrequency (%)
635
98.8%
 8
 
1.2%
Math Symbol
ValueCountFrequency (%)
>56
50.0%
<56
50.0%
Dash Punctuation
ValueCountFrequency (%)
-19
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3454
78.7%
Common930
 
21.2%
Cyrillic3
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n367
 
10.6%
e366
 
10.6%
a350
 
10.1%
t274
 
7.9%
i223
 
6.5%
s223
 
6.5%
r221
 
6.4%
o213
 
6.2%
h135
 
3.9%
l127
 
3.7%
Other values (40)955
27.6%
Common
ValueCountFrequency (%)
635
68.3%
>56
 
6.0%
<56
 
6.0%
.39
 
4.2%
,36
 
3.9%
/35
 
3.8%
-19
 
2.0%
'12
 
1.3%
010
 
1.1%
 8
 
0.9%
Other values (8)24
 
2.6%
Cyrillic
ValueCountFrequency (%)
В1
33.3%
Т1
33.3%
Н1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4376
99.7%
None8
 
0.2%
Cyrillic3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
635
14.5%
n367
 
8.4%
e366
 
8.4%
a350
 
8.0%
t274
 
6.3%
i223
 
5.1%
s223
 
5.1%
r221
 
5.1%
o213
 
4.9%
h135
 
3.1%
Other values (57)1369
31.3%
None
ValueCountFrequency (%)
 8
100.0%
Cyrillic
ValueCountFrequency (%)
В1
33.3%
Т1
33.3%
Н1
33.3%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45522.58824
Minimum802
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:05.196349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum802
5-th percentile7967.6
Q142412
median52108
Q352496
95-th percentile57207.4
Maximum61755
Range60953
Interquartile range (IQR)10084

Descriptive statistics

Standard deviation14243.37787
Coefficient of variation (CV)0.3128859413
Kurtosis2.572272323
Mean45522.58824
Median Absolute Deviation (MAD)1356
Skewness-1.867754523
Sum3869420
Variance202873813.1
MonotonicityNot monotonic
2022-05-09T21:04:05.306180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
527308
 
9.4%
524226
 
7.1%
519545
 
5.9%
522724
 
4.7%
521812
 
2.4%
152502
 
2.4%
522872
 
2.4%
588212
 
2.4%
549962
 
2.4%
521592
 
2.4%
Other values (47)50
58.8%
ValueCountFrequency (%)
8021
1.2%
25041
1.2%
60901
1.2%
61461
1.2%
61471
1.2%
152502
2.4%
175841
1.2%
189711
1.2%
224731
1.2%
262681
1.2%
ValueCountFrequency (%)
617551
1.2%
588212
2.4%
583671
1.2%
572571
1.2%
570091
1.2%
566551
1.2%
550191
1.2%
549962
2.4%
546101
1.2%
539751
1.2%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size808.0 B
https://www.tvmaze.com/shows/52730/fixer
https://www.tvmaze.com/shows/52422/light-speed
 
6
https://www.tvmaze.com/shows/51954/the-runner
 
5
https://www.tvmaze.com/shows/52272/room-2806-the-accusation
 
4
https://www.tvmaze.com/shows/52181/volk
 
2
Other values (52)
60 

Length

Max length79
Median length60
Mean length49.32941176
Min length39

Characters and Unicode

Total characters4193
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)51.8%

Sample

1st rowhttps://www.tvmaze.com/shows/52181/volk
2nd rowhttps://www.tvmaze.com/shows/52181/volk
3rd rowhttps://www.tvmaze.com/shows/52198/kotiki
4th rowhttps://www.tvmaze.com/shows/56655/going-seventeen
5th rowhttps://www.tvmaze.com/shows/50916/my-little-invisible-being

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52730/fixer8
 
9.4%
https://www.tvmaze.com/shows/52422/light-speed6
 
7.1%
https://www.tvmaze.com/shows/51954/the-runner5
 
5.9%
https://www.tvmaze.com/shows/52272/room-2806-the-accusation4
 
4.7%
https://www.tvmaze.com/shows/52181/volk2
 
2.4%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.4%
https://www.tvmaze.com/shows/52287/inside-poundland-secrets-from-the-shop-floor2
 
2.4%
https://www.tvmaze.com/shows/58821/tunelis2
 
2.4%
https://www.tvmaze.com/shows/54996/the-silent-criminal2
 
2.4%
https://www.tvmaze.com/shows/52159/to-love2
 
2.4%
Other values (47)50
58.8%

Length

2022-05-09T21:04:05.432357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52730/fixer8
 
9.4%
https://www.tvmaze.com/shows/52422/light-speed6
 
7.1%
https://www.tvmaze.com/shows/51954/the-runner5
 
5.9%
https://www.tvmaze.com/shows/52272/room-2806-the-accusation4
 
4.7%
https://www.tvmaze.com/shows/54996/the-silent-criminal2
 
2.4%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.4%
https://www.tvmaze.com/shows/52108/psych-hunter2
 
2.4%
https://www.tvmaze.com/shows/52159/to-love2
 
2.4%
https://www.tvmaze.com/shows/42412/professionals2
 
2.4%
https://www.tvmaze.com/shows/58821/tunelis2
 
2.4%
Other values (47)50
58.8%

Most occurring characters

ValueCountFrequency (%)
/425
 
10.1%
w354
 
8.4%
t347
 
8.3%
s325
 
7.8%
o247
 
5.9%
e231
 
5.5%
h223
 
5.3%
m200
 
4.8%
.170
 
4.1%
a150
 
3.6%
Other values (30)1521
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2945
70.2%
Other Punctuation680
 
16.2%
Decimal Number437
 
10.4%
Dash Punctuation131
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w354
12.0%
t347
11.8%
s325
11.0%
o247
 
8.4%
e231
 
7.8%
h223
 
7.6%
m200
 
6.8%
a150
 
5.1%
c115
 
3.9%
p112
 
3.8%
Other values (16)641
21.8%
Decimal Number
ValueCountFrequency (%)
586
19.7%
280
18.3%
144
10.1%
041
9.4%
440
9.2%
932
 
7.3%
731
 
7.1%
830
 
6.9%
628
 
6.4%
325
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/425
62.5%
.170
 
25.0%
:85
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2945
70.2%
Common1248
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
w354
12.0%
t347
11.8%
s325
11.0%
o247
 
8.4%
e231
 
7.8%
h223
 
7.6%
m200
 
6.8%
a150
 
5.1%
c115
 
3.9%
p112
 
3.8%
Other values (16)641
21.8%
Common
ValueCountFrequency (%)
/425
34.1%
.170
 
13.6%
-131
 
10.5%
586
 
6.9%
:85
 
6.8%
280
 
6.4%
144
 
3.5%
041
 
3.3%
440
 
3.2%
932
 
2.6%
Other values (4)114
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/425
 
10.1%
w354
 
8.4%
t347
 
8.3%
s325
 
7.8%
o247
 
5.9%
e231
 
5.5%
h223
 
5.3%
m200
 
4.8%
.170
 
4.1%
a150
 
3.6%
Other values (30)1521
36.3%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size808.0 B
Fixer
Light Speed
 
6
The Runner
 
5
Room 2806: The Accusation
 
4
Волк
 
2
Other values (52)
60 

Length

Max length45
Median length21
Mean length14.57647059
Min length4

Characters and Unicode

Total characters1239
Distinct characters87
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)51.8%

Sample

1st rowВолк
2nd rowВолк
3rd rowКотики
4th rowGoing Seventeen
5th rowMy Little Invisible Being

Common Values

ValueCountFrequency (%)
Fixer8
 
9.4%
Light Speed6
 
7.1%
The Runner5
 
5.9%
Room 2806: The Accusation4
 
4.7%
Волк2
 
2.4%
The Young Turks2
 
2.4%
Inside Poundland: Secrets from the Shop Floor2
 
2.4%
Tunelis2
 
2.4%
The Silent Criminal2
 
2.4%
To Love2
 
2.4%
Other values (47)50
58.8%

Length

2022-05-09T21:04:05.542066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the22
 
10.2%
fixer8
 
3.7%
speed6
 
2.8%
light6
 
2.8%
of5
 
2.3%
runner5
 
2.3%
room4
 
1.9%
28064
 
1.9%
accusation4
 
1.9%
love4
 
1.9%
Other values (123)148
68.5%

Most occurring characters

ValueCountFrequency (%)
e135
 
10.9%
131
 
10.6%
n73
 
5.9%
o67
 
5.4%
i61
 
4.9%
r59
 
4.8%
h50
 
4.0%
t50
 
4.0%
s46
 
3.7%
a45
 
3.6%
Other values (77)522
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter881
71.1%
Uppercase Letter195
 
15.7%
Space Separator131
 
10.6%
Decimal Number18
 
1.5%
Other Punctuation14
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e135
15.3%
n73
 
8.3%
o67
 
7.6%
i61
 
6.9%
r59
 
6.7%
h50
 
5.7%
t50
 
5.7%
s46
 
5.2%
a45
 
5.1%
u38
 
4.3%
Other values (38)257
29.2%
Uppercase Letter
ValueCountFrequency (%)
T38
19.5%
S21
10.8%
R16
 
8.2%
F15
 
7.7%
A14
 
7.2%
M11
 
5.6%
L11
 
5.6%
P9
 
4.6%
W9
 
4.6%
C8
 
4.1%
Other values (17)43
22.1%
Other Punctuation
ValueCountFrequency (%)
:6
42.9%
'3
21.4%
.2
 
14.3%
!1
 
7.1%
,1
 
7.1%
?1
 
7.1%
Decimal Number
ValueCountFrequency (%)
05
27.8%
84
22.2%
24
22.2%
64
22.2%
31
 
5.6%
Space Separator
ValueCountFrequency (%)
131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1008
81.4%
Common163
 
13.2%
Cyrillic68
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e135
 
13.4%
n73
 
7.2%
o67
 
6.6%
i61
 
6.1%
r59
 
5.9%
h50
 
5.0%
t50
 
5.0%
s46
 
4.6%
a45
 
4.5%
T38
 
3.8%
Other values (38)384
38.1%
Cyrillic
ValueCountFrequency (%)
о9
13.2%
е6
 
8.8%
к5
 
7.4%
и5
 
7.4%
р4
 
5.9%
т4
 
5.9%
п4
 
5.9%
н4
 
5.9%
л4
 
5.9%
В4
 
5.9%
Other values (17)19
27.9%
Common
ValueCountFrequency (%)
131
80.4%
:6
 
3.7%
05
 
3.1%
84
 
2.5%
24
 
2.5%
64
 
2.5%
'3
 
1.8%
.2
 
1.2%
!1
 
0.6%
31
 
0.6%
Other values (2)2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1168
94.3%
Cyrillic68
 
5.5%
None3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e135
 
11.6%
131
 
11.2%
n73
 
6.2%
o67
 
5.7%
i61
 
5.2%
r59
 
5.1%
h50
 
4.3%
t50
 
4.3%
s46
 
3.9%
a45
 
3.9%
Other values (47)451
38.6%
Cyrillic
ValueCountFrequency (%)
о9
13.2%
е6
 
8.8%
к5
 
7.4%
и5
 
7.4%
р4
 
5.9%
т4
 
5.9%
п4
 
5.9%
н4
 
5.9%
л4
 
5.9%
В4
 
5.9%
Other values (17)19
27.9%
None
ValueCountFrequency (%)
ø1
33.3%
ä1
33.3%
Ç1
33.3%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size808.0 B
Scripted
41 
Documentary
16 
Talk Show
Reality
Animation
Other values (4)

Length

Max length11
Median length9
Mean length8.470588235
Min length4

Characters and Unicode

Total characters720
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowScripted
2nd rowScripted
3rd rowScripted
4th rowVariety
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted41
48.2%
Documentary16
 
18.8%
Talk Show8
 
9.4%
Reality7
 
8.2%
Animation6
 
7.1%
News3
 
3.5%
Variety2
 
2.4%
Game Show1
 
1.2%
Sports1
 
1.2%

Length

2022-05-09T21:04:05.651701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:04:05.766349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted41
43.6%
documentary16
 
17.0%
show9
 
9.6%
talk8
 
8.5%
reality7
 
7.4%
animation6
 
6.4%
news3
 
3.2%
variety2
 
2.1%
game1
 
1.1%
sports1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
t73
 
10.1%
e70
 
9.7%
i62
 
8.6%
r60
 
8.3%
c57
 
7.9%
S51
 
7.1%
p42
 
5.8%
d41
 
5.7%
a40
 
5.6%
o32
 
4.4%
Other values (17)192
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter617
85.7%
Uppercase Letter94
 
13.1%
Space Separator9
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t73
11.8%
e70
11.3%
i62
10.0%
r60
9.7%
c57
9.2%
p42
 
6.8%
d41
 
6.6%
a40
 
6.5%
o32
 
5.2%
n28
 
4.5%
Other values (8)112
18.2%
Uppercase Letter
ValueCountFrequency (%)
S51
54.3%
D16
 
17.0%
T8
 
8.5%
R7
 
7.4%
A6
 
6.4%
N3
 
3.2%
V2
 
2.1%
G1
 
1.1%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin711
98.8%
Common9
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t73
 
10.3%
e70
 
9.8%
i62
 
8.7%
r60
 
8.4%
c57
 
8.0%
S51
 
7.2%
p42
 
5.9%
d41
 
5.8%
a40
 
5.6%
o32
 
4.5%
Other values (16)183
25.7%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t73
 
10.1%
e70
 
9.7%
i62
 
8.6%
r60
 
8.3%
c57
 
7.9%
S51
 
7.1%
p42
 
5.8%
d41
 
5.7%
a40
 
5.6%
o32
 
4.4%
Other values (17)192
26.7%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size808.0 B
English
33 
Chinese
16 
Arabic
10 
Russian
Norwegian
 
3
Other values (10)
15 

Length

Max length10
Median length7
Mean length6.776470588
Min length3

Characters and Unicode

Total characters576
Distinct characters31
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.9%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowKorean
5th rowChinese

Common Values

ValueCountFrequency (%)
English33
38.8%
Chinese16
18.8%
Arabic10
 
11.8%
Russian8
 
9.4%
Norwegian3
 
3.5%
Korean2
 
2.4%
Dutch2
 
2.4%
Thai2
 
2.4%
nan2
 
2.4%
Latvian2
 
2.4%
Other values (5)5
 
5.9%

Length

2022-05-09T21:04:05.875934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english33
38.8%
chinese16
18.8%
arabic10
 
11.8%
russian8
 
9.4%
norwegian3
 
3.5%
korean2
 
2.4%
dutch2
 
2.4%
thai2
 
2.4%
nan2
 
2.4%
latvian2
 
2.4%
Other values (5)5
 
5.9%

Most occurring characters

ValueCountFrequency (%)
i78
13.5%
n71
12.3%
s68
11.8%
h56
9.7%
e41
 
7.1%
g37
 
6.4%
a34
 
5.9%
E33
 
5.7%
l33
 
5.7%
r18
 
3.1%
Other values (21)107
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter493
85.6%
Uppercase Letter83
 
14.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i78
15.8%
n71
14.4%
s68
13.8%
h56
11.4%
e41
8.3%
g37
7.5%
a34
6.9%
l33
6.7%
r18
 
3.7%
u14
 
2.8%
Other values (9)43
8.7%
Uppercase Letter
ValueCountFrequency (%)
E33
39.8%
C16
19.3%
A10
 
12.0%
R8
 
9.6%
N3
 
3.6%
T3
 
3.6%
L3
 
3.6%
K2
 
2.4%
D2
 
2.4%
P1
 
1.2%
Other values (2)2
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i78
13.5%
n71
12.3%
s68
11.8%
h56
9.7%
e41
 
7.1%
g37
 
6.4%
a34
 
5.9%
E33
 
5.7%
l33
 
5.7%
r18
 
3.1%
Other values (21)107
18.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i78
13.5%
n71
12.3%
s68
11.8%
h56
9.7%
e41
 
7.1%
g37
 
6.4%
a34
 
5.9%
E33
 
5.7%
l33
 
5.7%
r18
 
3.1%
Other values (21)107
18.6%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size808.0 B
[]
32 
['Drama', 'Comedy', 'Action']
['Comedy']
['Drama']
['Crime']
Other values (19)
28 

Length

Max length33
Median length32
Mean length13.41176471
Min length2

Characters and Unicode

Total characters1140
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)12.9%

Sample

1st row['Drama', 'Adventure', 'Mystery']
2nd row['Drama', 'Adventure', 'Mystery']
3rd row['Comedy']
4th row[]
5th row['Comedy', 'Anime']

Common Values

ValueCountFrequency (%)
[]32
37.6%
['Drama', 'Comedy', 'Action']8
 
9.4%
['Comedy']7
 
8.2%
['Drama']6
 
7.1%
['Crime']4
 
4.7%
['Drama', 'Comedy']3
 
3.5%
['Drama', 'Adventure', 'Mystery']2
 
2.4%
['Drama', 'Romance']2
 
2.4%
['Crime', 'Thriller', 'Mystery']2
 
2.4%
['Drama', 'Thriller', 'Mystery']2
 
2.4%
Other values (14)17
20.0%

Length

2022-05-09T21:04:05.985436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
32
22.9%
drama31
22.1%
comedy21
15.0%
action12
 
8.6%
crime9
 
6.4%
romance7
 
5.0%
thriller7
 
5.0%
mystery6
 
4.3%
adventure3
 
2.1%
sports3
 
2.1%
Other values (6)9
 
6.4%

Most occurring characters

ValueCountFrequency (%)
'216
18.9%
[85
 
7.5%
]85
 
7.5%
a72
 
6.3%
m72
 
6.3%
r69
 
6.1%
e60
 
5.3%
55
 
4.8%
,55
 
4.8%
o47
 
4.1%
Other values (21)324
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter536
47.0%
Other Punctuation271
23.8%
Uppercase Letter108
 
9.5%
Open Punctuation85
 
7.5%
Close Punctuation85
 
7.5%
Space Separator55
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a72
13.4%
m72
13.4%
r69
12.9%
e60
11.2%
o47
8.8%
y37
6.9%
i35
6.5%
n27
 
5.0%
t27
 
5.0%
d26
 
4.9%
Other values (7)64
11.9%
Uppercase Letter
ValueCountFrequency (%)
C31
28.7%
D31
28.7%
A18
16.7%
R7
 
6.5%
T7
 
6.5%
M6
 
5.6%
S3
 
2.8%
F3
 
2.8%
H2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
'216
79.7%
,55
 
20.3%
Open Punctuation
ValueCountFrequency (%)
[85
100.0%
Close Punctuation
ValueCountFrequency (%)
]85
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin644
56.5%
Common496
43.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a72
11.2%
m72
11.2%
r69
10.7%
e60
 
9.3%
o47
 
7.3%
y37
 
5.7%
i35
 
5.4%
C31
 
4.8%
D31
 
4.8%
n27
 
4.2%
Other values (16)163
25.3%
Common
ValueCountFrequency (%)
'216
43.5%
[85
 
17.1%
]85
 
17.1%
55
 
11.1%
,55
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'216
18.9%
[85
 
7.5%
]85
 
7.5%
a72
 
6.3%
m72
 
6.3%
r69
 
6.1%
e60
 
5.3%
55
 
4.8%
,55
 
4.8%
o47
 
4.1%
Other values (21)324
28.4%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
Ended
44 
Running
30 
To Be Determined
11 

Length

Max length16
Median length5
Mean length7.129411765
Min length5

Characters and Unicode

Total characters606
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowEnded
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Ended44
51.8%
Running30
35.3%
To Be Determined11
 
12.9%

Length

2022-05-09T21:04:06.079343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:04:06.173301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ended44
41.1%
running30
28.0%
to11
 
10.3%
be11
 
10.3%
determined11
 
10.3%

Most occurring characters

ValueCountFrequency (%)
n145
23.9%
d99
16.3%
e88
14.5%
E44
 
7.3%
i41
 
6.8%
R30
 
5.0%
u30
 
5.0%
g30
 
5.0%
22
 
3.6%
T11
 
1.8%
Other values (6)66
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter477
78.7%
Uppercase Letter107
 
17.7%
Space Separator22
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n145
30.4%
d99
20.8%
e88
18.4%
i41
 
8.6%
u30
 
6.3%
g30
 
6.3%
o11
 
2.3%
t11
 
2.3%
r11
 
2.3%
m11
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
E44
41.1%
R30
28.0%
T11
 
10.3%
B11
 
10.3%
D11
 
10.3%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin584
96.4%
Common22
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n145
24.8%
d99
17.0%
e88
15.1%
E44
 
7.5%
i41
 
7.0%
R30
 
5.1%
u30
 
5.1%
g30
 
5.1%
T11
 
1.9%
o11
 
1.9%
Other values (5)55
 
9.4%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n145
23.9%
d99
16.3%
e88
14.5%
E44
 
7.3%
i41
 
6.8%
R30
 
5.0%
u30
 
5.0%
g30
 
5.0%
22
 
3.6%
T11
 
1.8%
Other values (6)66
10.9%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22
Distinct (%)33.8%
Missing20
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean42.66153846
Minimum2
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:06.251654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q118
median40
Q360
95-th percentile120
Maximum180
Range178
Interquartile range (IQR)42

Descriptive statistics

Standard deviation33.36693579
Coefficient of variation (CV)0.7821315637
Kurtosis4.424812318
Mean42.66153846
Median Absolute Deviation (MAD)20
Skewness1.818908144
Sum2773
Variance1113.352404
MonotonicityNot monotonic
2022-05-09T21:04:06.346600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6011
12.9%
188
 
9.4%
457
 
8.2%
305
 
5.9%
104
 
4.7%
483
 
3.5%
53
 
3.5%
503
 
3.5%
1203
 
3.5%
152
 
2.4%
Other values (12)16
18.8%
(Missing)20
23.5%
ValueCountFrequency (%)
21
 
1.2%
53
 
3.5%
104
4.7%
121
 
1.2%
152
 
2.4%
188
9.4%
202
 
2.4%
221
 
1.2%
231
 
1.2%
252
 
2.4%
ValueCountFrequency (%)
1801
 
1.2%
1301
 
1.2%
1203
 
3.5%
901
 
1.2%
6011
12.9%
512
 
2.4%
503
 
3.5%
483
 
3.5%
457
8.2%
401
 
1.2%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)35.7%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean39.60714286
Minimum2
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:06.440786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.15
Q117
median33.5
Q350
95-th percentile115.5
Maximum181
Range179
Interquartile range (IQR)33

Descriptive statistics

Standard deviation31.22750481
Coefficient of variation (CV)0.7884311404
Kurtosis5.344192176
Mean39.60714286
Median Absolute Deviation (MAD)16.5
Skewness1.957880665
Sum3327
Variance975.1570568
MonotonicityNot monotonic
2022-05-09T21:04:06.538757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
509
 
10.6%
609
 
10.6%
178
 
9.4%
457
 
8.2%
217
 
8.2%
304
 
4.7%
254
 
4.7%
53
 
3.5%
103
 
3.5%
1203
 
3.5%
Other values (20)27
31.8%
ValueCountFrequency (%)
21
 
1.2%
53
3.5%
81
 
1.2%
91
 
1.2%
103
3.5%
111
 
1.2%
122
2.4%
141
 
1.2%
152
2.4%
161
 
1.2%
ValueCountFrequency (%)
1811
 
1.2%
1301
 
1.2%
1203
 
3.5%
901
 
1.2%
771
 
1.2%
761
 
1.2%
609
10.6%
509
10.6%
483
 
3.5%
457
8.2%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Memory size808.0 B
2020-12-07
25 
2020-11-23
2020-11-16
2020-11-09
2020-11-30
 
3
Other values (36)
39 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters850
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)38.8%

Sample

1st row2020-12-07
2nd row2020-12-07
3rd row2020-11-30
4th row2017-06-12
5th row2020-10-05

Common Values

ValueCountFrequency (%)
2020-12-0725
29.4%
2020-11-238
 
9.4%
2020-11-166
 
7.1%
2020-11-094
 
4.7%
2020-11-303
 
3.5%
2020-10-052
 
2.4%
2020-11-192
 
2.4%
2013-12-242
 
2.4%
2016-10-301
 
1.2%
2008-10-261
 
1.2%
Other values (31)31
36.5%

Length

2022-05-09T21:04:06.639633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-0725
29.4%
2020-11-238
 
9.4%
2020-11-166
 
7.1%
2020-11-094
 
4.7%
2020-11-303
 
3.5%
2020-10-052
 
2.4%
2020-11-192
 
2.4%
2013-12-242
 
2.4%
2003-04-011
 
1.2%
2017-06-121
 
1.2%
Other values (31)31
36.5%

Most occurring characters

ValueCountFrequency (%)
0224
26.4%
2194
22.8%
-170
20.0%
1143
16.8%
734
 
4.0%
926
 
3.1%
322
 
2.6%
811
 
1.3%
610
 
1.2%
58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number680
80.0%
Dash Punctuation170
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0224
32.9%
2194
28.5%
1143
21.0%
734
 
5.0%
926
 
3.8%
322
 
3.2%
811
 
1.6%
610
 
1.5%
58
 
1.2%
48
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0224
26.4%
2194
22.8%
-170
20.0%
1143
16.8%
734
 
4.0%
926
 
3.1%
322
 
2.6%
811
 
1.3%
610
 
1.2%
58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0224
26.4%
2194
22.8%
-170
20.0%
1143
16.8%
734
 
4.0%
926
 
3.1%
322
 
2.6%
811
 
1.3%
610
 
1.2%
58
 
0.9%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
41 
2020-12-07
19 
2020-12-14
2020-12-28
 
3
2021-01-18
 
2
Other values (10)
13 

Length

Max length10
Median length10
Mean length6.623529412
Min length3

Characters and Unicode

Total characters563
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.2%

Sample

1st row2020-12-28
2nd row2020-12-28
3rd row2020-12-11
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan41
48.2%
2020-12-0719
22.4%
2020-12-147
 
8.2%
2020-12-283
 
3.5%
2021-01-182
 
2.4%
2020-12-302
 
2.4%
2020-12-162
 
2.4%
2020-12-232
 
2.4%
2020-12-111
 
1.2%
2020-12-241
 
1.2%
Other values (5)5
 
5.9%

Length

2022-05-09T21:04:06.733868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan41
48.2%
2020-12-0719
22.4%
2020-12-147
 
8.2%
2020-12-283
 
3.5%
2021-01-182
 
2.4%
2020-12-302
 
2.4%
2020-12-162
 
2.4%
2020-12-232
 
2.4%
2020-12-111
 
1.2%
2020-12-241
 
1.2%
Other values (5)5
 
5.9%

Most occurring characters

ValueCountFrequency (%)
2138
24.5%
0110
19.5%
-88
15.6%
n82
14.6%
163
11.2%
a41
 
7.3%
720
 
3.6%
48
 
1.4%
86
 
1.1%
34
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number352
62.5%
Lowercase Letter123
 
21.8%
Dash Punctuation88
 
15.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2138
39.2%
0110
31.2%
163
17.9%
720
 
5.7%
48
 
2.3%
86
 
1.7%
34
 
1.1%
63
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
n82
66.7%
a41
33.3%
Dash Punctuation
ValueCountFrequency (%)
-88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common440
78.2%
Latin123
 
21.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2138
31.4%
0110
25.0%
-88
20.0%
163
14.3%
720
 
4.5%
48
 
1.8%
86
 
1.4%
34
 
0.9%
63
 
0.7%
Latin
ValueCountFrequency (%)
n82
66.7%
a41
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2138
24.5%
0110
19.5%
-88
15.6%
n82
14.6%
163
11.2%
a41
 
7.3%
720
 
3.6%
48
 
1.4%
86
 
1.1%
34
 
0.7%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
https://shahid.mbc.net/en/series/Fixer/series-820315
https://www.youtube.com/playlist?list=PL6uC-XGZC7X7nu1ycW1YGbl-wErl027uc
https://www.netflix.com/title/81068760
 
4
https://premier.one/show/12339
 
2
Other values (49)
57 

Length

Max length130
Median length77
Mean length47.87058824
Min length3

Characters and Unicode

Total characters4069
Distinct characters76
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)48.2%

Sample

1st rowhttps://premier.one/show/12339
2nd rowhttps://premier.one/show/12339
3rd rowhttp://epic-media.ru/project/kotiki
4th rownan
5th rowhttps://www.bilibili.com/bangumi/media/md28229943/

Common Values

ValueCountFrequency (%)
nan8
 
9.4%
https://shahid.mbc.net/en/series/Fixer/series-8203158
 
9.4%
https://www.youtube.com/playlist?list=PL6uC-XGZC7X7nu1ycW1YGbl-wErl027uc6
 
7.1%
https://www.netflix.com/title/810687604
 
4.7%
https://premier.one/show/123392
 
2.4%
https://www.tytnetwork.com2
 
2.4%
https://www.channel4.com/programmes/inside-poundland-secrets-from-the-shop-floor2
 
2.4%
https://go3.tv/series/tunnel,serial-22014172
 
2.4%
https://www.iqiyi.com/a_je0t80m6td.html2
 
2.4%
https://so.youku.com/search_video/q_%20%E6%9C%80%E5%88%9D%E7%9A%84%E7%9B%B8%E9%81%87?searchfrom=12
 
2.4%
Other values (44)47
55.3%

Length

2022-05-09T21:04:06.833280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan8
 
9.4%
https://shahid.mbc.net/en/series/fixer/series-8203158
 
9.4%
https://www.youtube.com/playlist?list=pl6uc-xgzc7x7nu1ycw1ygbl-werl027uc6
 
7.1%
https://www.netflix.com/title/810687604
 
4.7%
https://www.iqiyi.com/a_je0t80m6td.html2
 
2.4%
https://www.iqiyi.com/a_19rrhskr95.html2
 
2.4%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.4%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
2.4%
https://viaplay.no/serier/professionals2
 
2.4%
https://go3.tv/series/tunnel,serial-22014172
 
2.4%
Other values (44)47
55.3%

Most occurring characters

ValueCountFrequency (%)
/329
 
8.1%
t301
 
7.4%
e246
 
6.0%
s241
 
5.9%
o165
 
4.1%
h156
 
3.8%
w155
 
3.8%
i145
 
3.6%
.143
 
3.5%
r140
 
3.4%
Other values (66)2048
50.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2660
65.4%
Other Punctuation635
 
15.6%
Decimal Number413
 
10.1%
Uppercase Letter227
 
5.6%
Dash Punctuation91
 
2.2%
Math Symbol28
 
0.7%
Connector Punctuation15
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t301
 
11.3%
e246
 
9.2%
s241
 
9.1%
o165
 
6.2%
h156
 
5.9%
w155
 
5.8%
i145
 
5.5%
r140
 
5.3%
n129
 
4.8%
p125
 
4.7%
Other values (16)857
32.2%
Uppercase Letter
ValueCountFrequency (%)
E25
 
11.0%
C24
 
10.6%
G16
 
7.0%
X15
 
6.6%
B14
 
6.2%
A13
 
5.7%
P13
 
5.7%
L12
 
5.3%
F12
 
5.3%
D9
 
4.0%
Other values (16)74
32.6%
Decimal Number
ValueCountFrequency (%)
055
13.3%
155
13.3%
845
10.9%
745
10.9%
242
10.2%
436
8.7%
636
8.7%
534
8.2%
933
8.0%
332
7.7%
Other Punctuation
ValueCountFrequency (%)
/329
51.8%
.143
22.5%
:77
 
12.1%
%57
 
9.0%
?14
 
2.2%
&10
 
1.6%
,3
 
0.5%
!1
 
0.2%
#1
 
0.2%
Math Symbol
ValueCountFrequency (%)
=25
89.3%
+2
 
7.1%
~1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
-91
100.0%
Connector Punctuation
ValueCountFrequency (%)
_15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2887
71.0%
Common1182
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t301
 
10.4%
e246
 
8.5%
s241
 
8.3%
o165
 
5.7%
h156
 
5.4%
w155
 
5.4%
i145
 
5.0%
r140
 
4.8%
n129
 
4.5%
p125
 
4.3%
Other values (42)1084
37.5%
Common
ValueCountFrequency (%)
/329
27.8%
.143
12.1%
-91
 
7.7%
:77
 
6.5%
%57
 
4.8%
055
 
4.7%
155
 
4.7%
845
 
3.8%
745
 
3.8%
242
 
3.6%
Other values (14)243
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/329
 
8.1%
t301
 
7.4%
e246
 
6.0%
s241
 
5.9%
o165
 
4.1%
h156
 
3.8%
w155
 
3.8%
i145
 
3.6%
.143
 
3.5%
r140
 
3.4%
Other values (66)2048
50.3%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.03529412
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:07.063870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median20
Q332
95-th percentile81.8
Maximum97
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.20583212
Coefficient of variation (CV)0.8583532332
Kurtosis1.817195891
Mean27.03529412
Median Absolute Deviation (MAD)9
Skewness1.484966148
Sum2298
Variance538.5106443
MonotonicityNot monotonic
2022-05-09T21:04:07.189485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
39
 
10.6%
159
 
10.6%
137
 
8.2%
256
 
7.1%
425
 
5.9%
184
 
4.7%
293
 
3.5%
323
 
3.5%
273
 
3.5%
63
 
3.5%
Other values (29)33
38.8%
ValueCountFrequency (%)
11
 
1.2%
39
10.6%
41
 
1.2%
63
 
3.5%
71
 
1.2%
81
 
1.2%
101
 
1.2%
112
 
2.4%
137
8.2%
141
 
1.2%
ValueCountFrequency (%)
972
2.4%
941
1.2%
871
1.2%
831
1.2%
771
1.2%
661
1.2%
651
1.2%
641
1.2%
631
1.2%
581
1.2%

_embedded_show_dvdCountry
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
85 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters255
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan85
100.0%

Length

2022-05-09T21:04:07.283795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:04:07.377699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan85
100.0%

Most occurring characters

ValueCountFrequency (%)
n170
66.7%
a85
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter255
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n170
66.7%
a85
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin255
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n170
66.7%
a85
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n170
66.7%
a85
33.3%

_embedded_show_summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
16 
<p>When an Arab celebrity has a problem, fixer Tony Tabet is the solution. Now he wants out to reconnect with his son, and with one month left on the job.</p>
<p>A race against time and the elements using high tech that could go fatally wrong - this is Light Speed. Join Derek Muller of Veritasium to meet the minds (and understand the physics!) behind the world's most advanced solar vehicles as they race 2,000 miles across the Australian Outback.</p>
<p><b>Room 2806: The Accusation</b> traces the 2011 sexual assault case involving French politician Dominique Strauss, who was then at the height of his career. On 14 May 2011, Nafissatou Diallo, a 32-year-old maid at the Sofitel New York Hotel, alleged that Strauss-Kahn had sexually assaulted her after she entered his suite. Hear the full story from people who were involved in the alleged incident and subsequent trial.</p>
 
4
<p><b>Professionals</b> is set against a backdrop of international espionage and corporate sabotage in the 21st century's privately-funded space race and follows hardened former counterintelligence officer Captain Vincent Corbo. After their advanced medical satellite explodes on deployment, billionaire futurist Peter Swann and his fiancée, medical visionary Dr. Graciela "Grace" Davila, turn to Corbo. Corbo assembles a team of experienced professionals to investigate the incident. They learn that any combination of Swann's business rivals, corrupt governments officials, and a shadowy crime syndicate could be behind the attack and represent a continued threat.</p>
 
2
Other values (43)
49 

Length

Max length877
Median length570
Mean length259.8117647
Min length3

Characters and Unicode

Total characters22084
Distinct characters86
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)43.5%

Sample

1st rownan
2nd rownan
3rd rownan
4th row<p>Initially a series of behind-the-scenes vlogs, <b>Going Seventeen</b> has taken a more structured route since mid-2019 and is now a reality-variety show with themed episodes. Every week, the members of Seventeen play games or participate in a variety of activities for everyone's delight and entertainment. Season 2021's keyword is "Watch What You Say", meaning that anything the members say can and will be turned into content...</p>
5th row<p>One day in 20XX, the alien pig prince who planned to take a human body as his home arrived on Earth, but unexpectedly discovered that the human being he wanted to live in had not yet been born! The pig prince, who has nowhere to settle down, got to know Saiji and Rubi. The three pulled various funny pranks on humans, causing humans to have baldness, bad breath, headaches, emotional crisis and other problems.</p>

Common Values

ValueCountFrequency (%)
nan16
18.8%
<p>When an Arab celebrity has a problem, fixer Tony Tabet is the solution. Now he wants out to reconnect with his son, and with one month left on the job.</p>8
 
9.4%
<p>A race against time and the elements using high tech that could go fatally wrong - this is Light Speed. Join Derek Muller of Veritasium to meet the minds (and understand the physics!) behind the world's most advanced solar vehicles as they race 2,000 miles across the Australian Outback.</p>6
 
7.1%
<p><b>Room 2806: The Accusation</b> traces the 2011 sexual assault case involving French politician Dominique Strauss, who was then at the height of his career. On 14 May 2011, Nafissatou Diallo, a 32-year-old maid at the Sofitel New York Hotel, alleged that Strauss-Kahn had sexually assaulted her after she entered his suite. Hear the full story from people who were involved in the alleged incident and subsequent trial.</p>4
 
4.7%
<p><b>Professionals</b> is set against a backdrop of international espionage and corporate sabotage in the 21st century's privately-funded space race and follows hardened former counterintelligence officer Captain Vincent Corbo. After their advanced medical satellite explodes on deployment, billionaire futurist Peter Swann and his fiancée, medical visionary Dr. Graciela "Grace" Davila, turn to Corbo. Corbo assembles a team of experienced professionals to investigate the incident. They learn that any combination of Swann's business rivals, corrupt governments officials, and a shadowy crime syndicate could be behind the attack and represent a continued threat.</p>2
 
2.4%
<p>From bargain to deluxe, Poundland are on a mission to transform their reputation and go upmarket, from investing millions on opening new stores and undergoing refits to launching new product ranges.</p>2
 
2.4%
<p>89 prisoners escaped from Pārlielupe Prison, tens of thousands of people were involved in their search, and mass arrests continued for ten years. Dramas of mutual relations, a massive and enigmatic escape is organized, in parallel with the flourishing of the love of the main character and the daughter of the head of the prison and the hopes for a new, beautiful life.</p>2
 
2.4%
<p>A story that follows people whose lives are entangled due to a complicated case. While investigating a drug cartel as an undercover cop, Yan Jin falls in love with the beautiful coffee shop owner Ji Xiao'ou.</p>2
 
2.4%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.4%
<p>Merchant Jiang Shuo and his odd specialist companion Qin Yi Heng purchase frequented houses to exchange them. In any case, alarming things start to occur and each spooky house is by all accounts part of a major riddle. Jiang Shuo, Yi Heng, and police officer Yuan Mu Qing attempt to understand the riddle.</p>2
 
2.4%
Other values (38)39
45.9%

Length

2022-05-09T21:04:07.487426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the214
 
5.9%
and131
 
3.6%
to99
 
2.7%
of88
 
2.4%
a88
 
2.4%
in59
 
1.6%
with46
 
1.3%
is40
 
1.1%
on35
 
1.0%
that31
 
0.8%
Other values (1239)2822
77.3%

Most occurring characters

ValueCountFrequency (%)
3564
16.1%
e2032
 
9.2%
t1446
 
6.5%
a1415
 
6.4%
n1329
 
6.0%
o1266
 
5.7%
i1174
 
5.3%
s1109
 
5.0%
r1008
 
4.6%
h862
 
3.9%
Other values (76)6879
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16611
75.2%
Space Separator3568
 
16.2%
Uppercase Letter722
 
3.3%
Other Punctuation590
 
2.7%
Math Symbol378
 
1.7%
Decimal Number148
 
0.7%
Dash Punctuation42
 
0.2%
Open Punctuation12
 
0.1%
Close Punctuation12
 
0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2032
12.2%
t1446
 
8.7%
a1415
 
8.5%
n1329
 
8.0%
o1266
 
7.6%
i1174
 
7.1%
s1109
 
6.7%
r1008
 
6.1%
h862
 
5.2%
l681
 
4.1%
Other values (21)4289
25.8%
Uppercase Letter
ValueCountFrequency (%)
T68
 
9.4%
S65
 
9.0%
A58
 
8.0%
W45
 
6.2%
D43
 
6.0%
Y42
 
5.8%
M41
 
5.7%
R33
 
4.6%
L32
 
4.4%
N27
 
3.7%
Other values (16)268
37.1%
Other Punctuation
ValueCountFrequency (%)
,226
38.3%
.175
29.7%
/96
16.3%
'46
 
7.8%
"22
 
3.7%
!13
 
2.2%
:8
 
1.4%
?2
 
0.3%
;1
 
0.2%
&1
 
0.2%
Decimal Number
ValueCountFrequency (%)
041
27.7%
235
23.6%
134
23.0%
38
 
5.4%
88
 
5.4%
98
 
5.4%
66
 
4.1%
44
 
2.7%
72
 
1.4%
52
 
1.4%
Space Separator
ValueCountFrequency (%)
3564
99.9%
 4
 
0.1%
Math Symbol
ValueCountFrequency (%)
<189
50.0%
>189
50.0%
Dash Punctuation
ValueCountFrequency (%)
-34
81.0%
8
 
19.0%
Open Punctuation
ValueCountFrequency (%)
(12
100.0%
Close Punctuation
ValueCountFrequency (%)
)12
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17333
78.5%
Common4751
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2032
11.7%
t1446
 
8.3%
a1415
 
8.2%
n1329
 
7.7%
o1266
 
7.3%
i1174
 
6.8%
s1109
 
6.4%
r1008
 
5.8%
h862
 
5.0%
l681
 
3.9%
Other values (47)5011
28.9%
Common
ValueCountFrequency (%)
3564
75.0%
,226
 
4.8%
<189
 
4.0%
>189
 
4.0%
.175
 
3.7%
/96
 
2.0%
'46
 
1.0%
041
 
0.9%
235
 
0.7%
-34
 
0.7%
Other values (19)156
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII22065
99.9%
None11
 
< 0.1%
Punctuation8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3564
16.2%
e2032
 
9.2%
t1446
 
6.6%
a1415
 
6.4%
n1329
 
6.0%
o1266
 
5.7%
i1174
 
5.3%
s1109
 
5.0%
r1008
 
4.6%
h862
 
3.9%
Other values (69)6860
31.1%
Punctuation
ValueCountFrequency (%)
8
100.0%
None
ValueCountFrequency (%)
 4
36.4%
ā2
18.2%
é2
18.2%
ç1
 
9.1%
ı1
 
9.1%
å1
 
9.1%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631057754
Minimum1602172227
Maximum1652004708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:04:07.598098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1602172227
5-th percentile1608999255
Q11614622943
median1636236331
Q31648029023
95-th percentile1651823692
Maximum1652004708
Range49832481
Interquartile range (IQR)33406080

Descriptive statistics

Standard deviation16885338.5
Coefficient of variation (CV)0.01035238541
Kurtosis-1.706824293
Mean1631057754
Median Absolute Deviation (MAD)15505216
Skewness-0.1296494677
Sum1.386399091 × 1011
Variance2.851146563 × 1014
MonotonicityNot monotonic
2022-05-09T21:04:07.723475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16146229438
 
9.4%
16089992556
 
7.1%
16151929545
 
5.9%
16158543454
 
4.7%
16404355312
 
2.4%
16481900582
 
2.4%
16073820732
 
2.4%
16362363312
 
2.4%
16196365812
 
2.4%
16090607262
 
2.4%
Other values (47)50
58.8%
ValueCountFrequency (%)
16021722271
 
1.2%
16073820732
 
2.4%
16089992556
7.1%
16090607262
 
2.4%
16095351412
 
2.4%
16114368421
 
1.2%
16130883481
 
1.2%
16133564461
 
1.2%
16146229438
9.4%
16151929545
5.9%
ValueCountFrequency (%)
16520047082
2.4%
16520040501
1.2%
16519332091
1.2%
16518386471
1.2%
16517638721
1.2%
16516373901
1.2%
16515703161
1.2%
16515025931
1.2%
16514293371
1.2%
16509088001
1.2%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/1950369
 
1
https://api.tvmaze.com/episodes/1998678
 
1
https://api.tvmaze.com/episodes/1998676
 
1
https://api.tvmaze.com/episodes/1998675
 
1
Other values (80)
80 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3315
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/19503691
 
1.2%
https://api.tvmaze.com/episodes/19986781
 
1.2%
https://api.tvmaze.com/episodes/19986761
 
1.2%
https://api.tvmaze.com/episodes/19986751
 
1.2%
https://api.tvmaze.com/episodes/19986741
 
1.2%
https://api.tvmaze.com/episodes/19986731
 
1.2%
https://api.tvmaze.com/episodes/19978151
 
1.2%
https://api.tvmaze.com/episodes/19978141
 
1.2%
https://api.tvmaze.com/episodes/20833311
 
1.2%
Other values (75)75
88.2%

Length

2022-05-09T21:04:07.817243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/20158371
 
1.2%
https://api.tvmaze.com/episodes/19640001
 
1.2%
https://api.tvmaze.com/episodes/19954051
 
1.2%
https://api.tvmaze.com/episodes/20077601
 
1.2%
https://api.tvmaze.com/episodes/19857891
 
1.2%
https://api.tvmaze.com/episodes/20396221
 
1.2%
https://api.tvmaze.com/episodes/20396231
 
1.2%
https://api.tvmaze.com/episodes/23244271
 
1.2%
https://api.tvmaze.com/episodes/23244281
 
1.2%
Other values (75)75
88.2%

Most occurring characters

ValueCountFrequency (%)
/340
 
10.3%
p255
 
7.7%
s255
 
7.7%
e255
 
7.7%
t255
 
7.7%
o170
 
5.1%
a170
 
5.1%
i170
 
5.1%
.170
 
5.1%
m170
 
5.1%
Other values (16)1105
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2125
64.1%
Other Punctuation595
 
17.9%
Decimal Number595
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p255
12.0%
s255
12.0%
e255
12.0%
t255
12.0%
o170
8.0%
a170
8.0%
i170
8.0%
m170
8.0%
h85
 
4.0%
d85
 
4.0%
Other values (3)255
12.0%
Decimal Number
ValueCountFrequency (%)
9108
18.2%
292
15.5%
180
13.4%
356
9.4%
054
9.1%
850
8.4%
643
 
7.2%
441
 
6.9%
737
 
6.2%
534
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/340
57.1%
.170
28.6%
:85
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2125
64.1%
Common1190
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/340
28.6%
.170
14.3%
9108
 
9.1%
292
 
7.7%
:85
 
7.1%
180
 
6.7%
356
 
4.7%
054
 
4.5%
850
 
4.2%
643
 
3.6%
Other values (3)112
 
9.4%
Latin
ValueCountFrequency (%)
p255
12.0%
s255
12.0%
e255
12.0%
t255
12.0%
o170
8.0%
a170
8.0%
i170
8.0%
m170
8.0%
h85
 
4.0%
d85
 
4.0%
Other values (3)255
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/340
 
10.3%
p255
 
7.7%
s255
 
7.7%
e255
 
7.7%
t255
 
7.7%
o170
 
5.1%
a170
 
5.1%
i170
 
5.1%
.170
 
5.1%
m170
 
5.1%
Other values (16)1105
33.3%

Interactions

2022-05-09T21:03:59.830847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:39.676601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:44.658977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:46.807644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:48.774534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:50.783180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:54.422438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:56.041321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:57.922608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:00.549013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:40.863273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:45.528485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:47.528273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:49.452559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:51.774084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.044472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:56.764946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:58.638916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:00.657986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:41.325081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:45.628709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:47.636312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:49.569207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:52.139064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.143174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:56.877411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:58.732563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:00.766552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:41.695538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:45.740243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:47.732593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:49.707829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:52.407907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.239655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:56.975239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:58.834550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:00.874875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:42.085780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:45.873467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:47.927440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:49.818272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:52.640080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.337639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:57.073325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:58.927694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:01.368161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:42.834504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:46.389366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:48.379085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:50.352773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:53.351383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.530954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:57.534725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:59.462107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:01.467357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:43.169061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:46.492301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:48.476880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:50.447937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:53.571326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.729774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:57.634419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:59.552040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:01.566286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:43.778275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:46.610341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:48.579473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:50.541013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:53.844073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.830378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:57.735315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:59.645620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:01.656508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:44.178648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:46.707003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:48.666811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:50.673061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:54.103473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:55.943261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:57.827532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:59.741269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:04:07.895959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:04:08.026177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:04:08.167260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:04:08.309608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:04:08.528708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:04:01.847265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:04:02.534533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:04:02.732695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:04:02.961113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01979245https://www.tvmaze.com/episodes/1979245/volk-1x01-seria-01Серия 011.01.0regular2020-12-07nan2020-12-07T00:00:00+00:0052.0None<p>НТВ 27.12.2021 https://www.ntv.ru/serial/Volk/</p>52181https://www.tvmaze.com/shows/52181/volkВолкScriptedRussian['Drama', 'Adventure', 'Mystery']Ended51.050.02020-12-072020-12-28https://premier.one/show/1233925.0nannan1.640436e+09https://api.tvmaze.com/episodes/1977902
11981560https://www.tvmaze.com/episodes/1981560/volk-1x02-seria-02Серия 021.02.0regular2020-12-07nan2020-12-07T00:00:00+00:0048.0Nonenan52181https://www.tvmaze.com/shows/52181/volkВолкScriptedRussian['Drama', 'Adventure', 'Mystery']Ended51.050.02020-12-072020-12-28https://premier.one/show/1233925.0nannan1.640436e+09https://api.tvmaze.com/episodes/2015818
21986869https://www.tvmaze.com/episodes/1986869/kotiki-1x06-seria-6Серия 61.06.0regular2020-12-07nan2020-12-07T00:00:00+00:0013.0Nonenan52198https://www.tvmaze.com/shows/52198/kotikiКотикиScriptedRussian['Comedy']Ended12.012.02020-11-302020-12-11http://epic-media.ru/project/kotiki15.0nannan1.637555e+09https://api.tvmaze.com/episodes/1964000
32140386https://www.tvmaze.com/episodes/2140386/going-seventeen-2020-12-07-dont-lie-ii-2Don't Lie Ⅱ #22020.041.0regular2020-12-07nan2020-12-07T03:00:00+00:0030.0Nonenan56655https://www.tvmaze.com/shows/56655/going-seventeenGoing SeventeenVarietyKorean[]Running30.030.02017-06-12nannan18.0nan<p>Initially a series of behind-the-scenes vlogs, <b>Going Seventeen</b> has taken a more structured route since mid-2019 and is now a reality-variety show with themed episodes. Every week, the members of Seventeen play games or participate in a variety of activities for everyone's delight and entertainment. Season 2021's keyword is "Watch What You Say", meaning that anything the members say can and will be turned into content...</p>1.651764e+09https://api.tvmaze.com/episodes/1995405
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